Identification of outcome predictors in transcranial direct current stimulation for major depression through explainable data-driven analysis

Identification of outcome predictors in transcranial direct current stimulation for major depression through explainable data-driven analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of outcome predictors in transcranial direct current stimulation for major depression through explainable data-driven analysis Sayna Rotbei, Giordano D’Urso, Marco Bortolomasi, Bernardo Dell’Osso, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6740366/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate and interpretable prediction of treatment outcomes is critical for advancing personalized interventions in major depressive disorder (MDD), particularly in the context of neuromodulatory therapies such as transcranial direct current stimulation (tDCS). Traditional approaches often lack precision and offer limited insight into the clinical profiles associated with treatment response. This study aims to identify key demographic and clinical predictors of tDCS efficacy in patients with depression, using interpretable, data-driven modeling. We analyzed a multi-center dataset comprising 169 patients with depression who underwent tDCS treatment. A supervised learning framework was employed to model treatment response, integrating explainable artificial intelligence techniques to enhance clinical interpretability. Predictive features included age, gender, baseline symptom severity, tDCS protocol, diagnostic subtype, comorbidities, illness duration, and concurrent medications. The model achieved a predictive accuracy of approximately 58% in classifying treatment responders and non-responders. Explainability analyses revealed that the most influential factors included age range (40–49 years), gender (female), medication status (drug-naive), and chronicity of illness (duration > 16 years). These characteristics defined the subgroup most likely to benefit from tDCS intervention. Our findings demonstrate the potential of explainable machine learning to support outcome prediction in tDCS therapy for depression. By highlighting clinically meaningful predictors and patient profiles, this approach may facilitate more personalized and effective treatment strategies in psychiatric care. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Psychology Health sciences/Health care Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6740366","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469182749,"identity":"e944a93c-5491-4426-b094-04c55e00d452","order_by":0,"name":"Sayna 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Traditional approaches often lack precision and offer limited insight into the clinical profiles associated with treatment response. This study aims to identify key demographic and clinical predictors of tDCS efficacy in patients with depression, using interpretable, data-driven modeling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe analyzed a multi-center dataset comprising 169 patients with depression who underwent tDCS treatment. A supervised learning framework was employed to model treatment response, integrating explainable artificial intelligence techniques to enhance clinical interpretability. Predictive features included age, gender, baseline symptom severity, tDCS protocol, diagnostic subtype, comorbidities, illness duration, and concurrent medications. The model achieved a predictive accuracy of approximately 58% in classifying treatment responders and non-responders. 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